{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Performance comparison copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import xgboost\n",
    "import numpy as np\n",
    "import shap\n",
    "import time\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pylab as pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from iml.common import convert_to_instance, convert_to_model, match_instance_to_data, match_model_to_data, convert_to_instance_with_index\n",
    "from iml.explanations import AdditiveExplanation\n",
    "from iml.links import convert_to_link, IdentityLink\n",
    "from iml.datatypes import convert_to_data, DenseData\n",
    "import logging\n",
    "from iml.explanations import AdditiveExplanation\n",
    "\n",
    "log = logging.getLogger('shap')\n",
    "from shap import KernelExplainer\n",
    "class IMEExplainer(KernelExplainer):\n",
    "    \"\"\" This is an implementation of the IME explanation method (aka. Shapley sampling values)\n",
    "    \n",
    "    This is implemented here for comparision and evaluation purposes, the KernelExplainer is\n",
    "    typically more efficient and so is the preferred model agnostic estimation method in this package.\n",
    "    IME was proposed in \"An Efficient Explanation of Individual Classifications using Game Theory\",\n",
    "    Erik Štrumbelj, Igor Kononenko, JMLR 2010\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, model, data, **kwargs):\n",
    "        # silence warning about large datasets\n",
    "        level = log.level\n",
    "        log.setLevel(logging.ERROR)\n",
    "        super(IMEExplainer, self).__init__(model, data, **kwargs)\n",
    "        log.setLevel(level)\n",
    "    \n",
    "    def explain(self, incoming_instance, **kwargs):\n",
    "        # convert incoming input to a standardized iml object\n",
    "        instance = convert_to_instance(incoming_instance)\n",
    "        match_instance_to_data(instance, self.data)\n",
    "        \n",
    "        # pick a reasonable number of samples if the user didn't specify how many they wanted\n",
    "        self.nsamples = kwargs.get(\"nsamples\", 0)\n",
    "        if self.nsamples == 0:\n",
    "            self.nsamples = 1000 * self.P\n",
    "        \n",
    "        # divide up the samples among the features\n",
    "        self.nsamples_each = np.ones(self.P, dtype=np.int64) * 2 * (self.nsamples // (self.P * 2))\n",
    "        for i in range((self.nsamples % (self.P * 2)) // 2):\n",
    "            self.nsamples_each[i] += 2\n",
    "        \n",
    "        model_out = self.model.f(instance.x)\n",
    "        \n",
    "        # explain every feature\n",
    "        phi = np.zeros(self.P)\n",
    "        self.X_masked = np.zeros((self.nsamples_each.max(), X.shape[1]))\n",
    "        for i in range(self.P):\n",
    "            phi[i] = self.ime(i, self.model.f, instance.x, self.data.data, nsamples=self.nsamples_each[i])\n",
    "        phi = np.array(phi)\n",
    "        \n",
    "        return AdditiveExplanation(self.link.f(1), self.link.f(1), phi, np.zeros(len(phi)), instance, self.link,\n",
    "                                   self.model, self.data)\n",
    "        \n",
    "        \n",
    "    def ime(self, j, f, x, X, nsamples=10):\n",
    "        assert nsamples % 2 == 0, \"nsamples must be divisible by 2!\"\n",
    "        X_masked = self.X_masked[:nsamples,:]\n",
    "        inds = np.arange(X.shape[1])\n",
    "\n",
    "        for i in range(0, nsamples//2):\n",
    "            np.random.shuffle(inds)\n",
    "            pos = np.where(inds == j)[0][0]\n",
    "            rind = np.random.randint(X.shape[0])\n",
    "            X_masked[i,:] = x\n",
    "            X_masked[i,inds[pos+1:]] = X[rind,inds[pos+1:]]\n",
    "            X_masked[-(i+1),:] = x\n",
    "            X_masked[-(i+1),inds[pos:]] = X[rind,inds[pos:]]\n",
    "        \n",
    "        s = time.time()\n",
    "        evals = f(X_masked)\n",
    "        #print(\"n\",time.time() - s)\n",
    "        \n",
    "        evals_on = evals[:nsamples//2]\n",
    "        evals_off = evals[nsamples//2:][::-1]\n",
    "        \n",
    "        return np.mean(evals[:nsamples//2] - evals[nsamples//2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 9/9 [00:26<00:00,  3.55s/it]\n"
     ]
    }
   ],
   "source": [
    "tree_shap_times = []\n",
    "sample_times = []\n",
    "Ms = [20,30,40,50,60,70,80,90,100]\n",
    "for M in tqdm(Ms):\n",
    "    \n",
    "    X = np.random.randn(N, M)\n",
    "    y = np.random.randn(N)\n",
    "    model = xgboost.train({\"eta\": 1}, xgboost.DMatrix(X, y), 1000)\n",
    "    \n",
    "    #print()\n",
    "    e = shap.TreeExplainer(model)\n",
    "    s = time.time()\n",
    "    e.shap_values(X)\n",
    "    tree_shap_times.append((time.time() - s)/1000)\n",
    "    #print((time.time() - s)/1000)\n",
    "    \n",
    "    tmp = np.vstack([X for i in range(1 * M)])\n",
    "    s = time.time()\n",
    "    model.predict(xgboost.DMatrix(tmp))\n",
    "    sample_times.append(time.time() - s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.633111  , 5.85872889, 5.49962997, 5.51635981, 5.31605005,\n",
       "       5.07364988, 4.98589039, 4.95893955, 4.9760294 , 4.86562967,\n",
       "       4.64447975])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(tree_shap_times)*10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hlpGF170b6NtnsWskSVJb89ZD8Mft6waGlTaGUx+H7b5uYJCaoUP/rSkser4TOAi4Djgh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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a13c17ef0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 390
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.plot(Ms, np.array(tree_shap_times[:-1])*10000 / (60))\n",
    "pl.plot(Ms, np.array(sample_times[:-1])*10000 / (60))\n",
    "pl.ylabel(\"minutes of runtime\")\n",
    "pl.xlabel(\"# of features\")\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.271293222904205"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(np.array(tree_shap_times[:-1])*10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "947.6979966976681"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4995.5940246/5.27129322"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "947.6979966976681"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4995.5940246/5.27129322"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1016.5784222579987"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_times[-5]/tree_shap_times[-5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4995.594024658203"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(np.array(sample_times[:-1])*10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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C0dGRdevWkSNHDgoXLmyxQmlCzZo1izp16vC///2PzZs3U61aNS5dusTKlSvJ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\n9v8BYwVfEhHpfEQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a16837c88>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 209,
       "width": 624
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = pl.figure(figsize=(10,3))\n",
    "pl.subplot(1, 2, 1)\n",
    "pl.plot(\n",
    "    Ms[:-1], np.array(sample_times[:-1])*10000 / (60),\n",
    "    label=\"Model agnostic sampling\",\n",
    "    color=\"#7C52FF\", linewidth=3\n",
    ")\n",
    "pl.plot(\n",
    "    Ms[:-1], np.array(tree_shap_times[:-1])*10000 / (60),\n",
    "    label=\"Tree SHAP\", color=\"#1E88E5\", linewidth=3\n",
    ")\n",
    "pl.ylabel(\"minutes of runtime\\nexplaining 10k predictions\")\n",
    "pl.xlabel(\"# of features in the model\")\n",
    "pl.legend()\n",
    "#pl.savefig(\"runtime.pdf\")\n",
    "#pl.show()\n",
    "\n",
    "pl.subplot(1, 2, 2)\n",
    "pl.plot(\n",
    "    Ms[:-1], (ime_std[:-1] / ime_m[:-1])*100, \"--\",\n",
    "    label=\"IME\", color=\"#7C52FF\", linewidth=3\n",
    ")\n",
    "pl.plot(\n",
    "    Ms[:-1], (kernel_shap_std[:-1] / kernel_shap_m[:-1])*100,\n",
    "    label=\"Kernel SHAP\",\n",
    "    color=\"#7C52FF\", linewidth=3\n",
    ")\n",
    "pl.plot(\n",
    "    Ms[:-1], np.zeros(len(Ms)-1),\n",
    "    label=\"Tree SHAP\",\n",
    "    color=\"#1E88E5\", linewidth=3\n",
    ")\n",
    "pl.ylabel(\"Std. deviation as % of magnitude\")\n",
    "pl.xlabel(\"# of features in the model\")\n",
    "pl.legend(loc=\"upper left\")\n",
    "pl.savefig(\"perf.pdf\")\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a8c8s/hTg4F5Xa39wX/7H/OOFF/KWpsQHgi/zUTJfCtbaHcaY/wFjUTIvIhIT\nMtbDq//2Hz/tYujSN7x4ROLRN8vgrRJ2Uq7bOPpL2ApvVHWMDwTIzXUJ/sG9kHcMxdjJVVxXmyCU\npoNPNAuzm80eoEWI9xMRkTI6sA9efMB/MV27njD40nBjilfWuvaVLTpFOhIJ287Nbj2KX4lK9wFw\n8nnhxhS2xES3BuBYWrZecKN7/bg+oOjsf6GnAH5rBAov5tfMfCkYY1KBYcCWMO4nIiLls3UT7PfZ\n4bV2Axj5S230EoR1X8HsSbDha7jmbmh9XKQjkrBkH3QLXv0WczZuDRfcEP2z8pFU3vUBBcl97YYV\nE19YgmpNeVUJ128OXA60Ax4K4n4iIlKxWnSEG+6HyQ/Clg2HjydVgctudTu9Stn9sA7mTIJViw8f\nmz0Jxt2n5K0ysBam/xsy1nmPp9VwC8urVPKdlCtKwfqAKlXjY/+HoGbmJ+LddrLgR1Ie8H/A7wO6\nn4iIVLC6TeC6++DVx2H5h+7YBTe4GUMpu68+cSVMRW1cBV9/Bp1PDD8mCdeHMw5/TxWVkACX3qKd\nlKX0gkrmr/Y5ngfsBBZaazMCupeIiIQkJRUuGQ/N2sHu7dDztEhHFPvadnfb1e/bXXxszmTo2Fsl\nTPFszVJ4e5L/+Nk/gTbdwotHYl9QfeafC+I6IiISfYyBU86PdBTxIyUVBo6AWU8XH9uyEZbNh+MH\nhh6WhGBHBrz8d/8Frz0HwknDQg1J4oDnZk/HyhhzlTGm+1HO6VZCbb2IiEil0ecM/zKKuS+5Lh0S\nX7IOwOT7/Re8NmkL54fQT17iTyDJPK5mfvhRzjkfeDag+4mIiMSspGQ43ae1564tsHB2uPFIxbIW\npj8Gmzd4j1fLX/CarAWvUgZBJfOlkYj3IlkREYmQ7IORjqDy6t4fGjT3Hps31c3kSnyYPx2+/Mh7\nLCERRv06PrqqSGSEmcx3wC2GFRGRKLB7G/zj5/DRLDdzKOFKSIQho73H9u12/18k9q1a7BY2+zln\nDLTqGlo4EofKvADWGPNMkUPDjTGtPE5NxO382h/QjyYRkShwKBtefBAyd8D/noHv1sD5N6ivddg6\n9YHmHd0OsEV9MANOPEs9/WPZ9h/glX/4v1k+fhD0PSfcmCT+lKebzZhCf7ZAz/yXFwt8Aowvx/1E\nRCQg/3vaJfAFlr3v6nkv+w3UaRS5uCobY+CM0fDMncXHsvbD+/91rQol9hQseD24z3u8aTs4Twte\nJQDlKbM1b4gDAAAgAElEQVRpnf9qg9sc6h+FjhV+tQBqWGtPttauLV+4IiJSXgtnw8I5xY9nrIP/\n/NY/+Yh1OYdg0Ry30+oXH0ZPaVHrrtD+eO+xT950/f0l9qxfAdu+9x6rVtPtpJxcJdyYJD6VeWbe\nWru+4M/GmLuBdwsfExGR6LNpNbw+wX/8lPOharXw4glLbq6b/d646vCxFZ/AJbdELqbChlwOq5cU\nP56TDfNehgtuDD8mKZ8OveDqP8KUh4/cICwh0T0Bq1k3crFJfAlkAay19m5r7ftBXEtERCrG3t2u\nTj43x3u8y0nQ/2hNhmPUZ28dmciDm51f+0Vk4imqSRs47hTvscVz/Wd4Jbq16go3PgBN2x4+Nuwa\naNk5cjFJ/ClTMm+MaZH/Sizy+VFfwYYvIiKlkZsLLz8Me3xKNuo1hYtuit/63UXveB9fviDcOEoy\nZBQkePxWzsuDd14MPx4JRs16cO2f3K6+vU+HE86KdEQSb8paZrMOt6i1M7Cq0OdHY8txTxERKaPZ\n/wfffuk9lpLqNqxJSQ03prD8sM6tB/CyarGrnY+GNzF1m0Cv0703jFq+APpf6GbwJfYkp8CFN7k3\nZtHwb03iS1kT6+dxifnuIp+LiEiU+eJD+HCm//hFP4f6zcKLJ2xL3/Uf273NdfFp1DK8eEoyaCQs\nfc/Vyhc1exL85A/hxyTBMAYSEyMdhcSjMiXz1toxJX0uIiLRIWO920bez4CLoEvf8OIJW24uLJtf\n8jkrF0VPMl+jLpx0jusxX9SapfDtcmh9XPhxiUj0CnMHWBERCdGBffDiA3Aoy3u8XQ84fVS4MYVt\nzdIjO4l4WbUonFhKq/+FkJLmPTZ7UvS01BQn51CkI5DKTsm8iEgcysuDqY/Ajgzv8VoNYOR41yYv\nni0pocSmwMZVsD+z4mMprbR0OPWC4seTq0CrLv7diCR8WzfBP26ClQsjHYlUZoEtRjXG1AGuAU4E\nagNevyKstfb0oO4pIiLe5k31n3FOquL6XKelhxtT2A7sha8/O/p5Ng9WL4Ue/Ss+ptLqNww+/p97\nqpCQ6LqgDBwJNepEOjIpcHCf2+F19zaY9FcYPMqVrXl1JBKpSIEk88aYTsA8oD5uN1g/ejgoIlLB\nVi6Ed1/yH7/g+srRFWX5gtLPYq9cGF3JfEoqDLrE7SJ6+iio2zjSEUlhBU++Cvr/W+vah36/Fi7+\nefx2hpLoFNT7x4eABsD9QBsg2Vqb4PGK8we6IiKRtf0Hl2T46XsO9BwYWjgRtWRe6c9ds9Qtlo0m\nfc+GS8YrkY9G777sFk4XteITePoPkBdl/5YkvgWVzPcHZllr77DWrrPW6p+xiEjIsg/C5Afg4H7v\n8Rad4OyfhBtTpGz7HjauLP35B/bCplVHP0/kq09g3iv+433Pjv+1KBJdgkrmDfBVQNcSEZFjZC28\n+m/YssF7PL02jPo1JCWHG1ekLJ3nfbxmPWjTzXvMa6ZVpLAtG2Hao/7jJ54FvYeEF48IBJfMLwI6\nBnQtERE5RlkHYOcW77HEJJfIp9cON6ZIyctzGy956TEAOvXxHlMyLyU5kL/gNfug93jLznDO1eHG\nJALBJfP3AEONMQMDup6IiByDqmlw7Z/ghDOLj51ztSuxqSzWfek6jHjpORA6+iTzWzbArq0VFpbE\nsLxcmPoPtybFS406levJl0SXoFpTNgdmAG8bY17EzdTv8jrRWvt8QPcUEZFCkpLh/OuhaVt4fYLb\nzOb4ge7Rf2XiV2LTvAPUb+r+XK/J4U4kha1aBCeeXWGhVYgdGVCnUaSjiG9zX4JVi73HkpLhsluh\neq1wYxIpEFQyPxHXdtIAV+a/irahNPnHlMyLiFSg3kOgYSt4byqcdx2YkhoGx5msA/Dlx95jhbv4\ndOjtncyvjKFk/rs1MHsyrP8Kfvkvtx5AgvflR/DeNP/x866DZu3Di0ekqKCSeVWJiYhEkWbtYPTt\nkY4ifCs+8a5pTkyC404+/HnH3rDgteLnrV0O2VlQJaXiYiyvrZvgnSkuySzw7isw/MbIxRSvNm+A\n//7Lf7zvOdBrcHjxiHgJJJm31j4XxHVERETKw6+3fKcTjtzxtkUnSEmDrCJtPHOy4dvlLtmPRgtn\nw8wn3a61hS2eC6ecB/WbRSaueHRgb8kLXlt1gXPGhBqSiCdtOiwiInFh9zaXiHs5fuCRnyclQ7se\n3ueuiuKuNq27em+zbvPcbL0EIy8XXvmHW4/gpUZduPTX7omPSKQpmRcRiSFZB1zrRSlu6Xuu335R\n1WpCu57Fj3fo5X2dlYu8rxMN6jaBXqd7j335kaujl/Kb8yKsXuI9lpQMl98K1WuGG5OIn0CSeWPM\n2lK81hhjFhtjJhljLg7iviIilUluLkz6K0z+q+t5LYdZ69/Fpnt/7xnUDr28Fwfv3uZqpaPVoJGQ\nVMV7bPbkcGOJR8sXwPzp/uPn3wBN24UXj8jRBDUznwBUAVrlv5oBqfkfC45VBdoBlwEvG2NeM8Zo\nw2MRkVKa/X+ujGTlInji1uhOOMO2abV3dxooXmJToHot/6QsmkttatSFk87xHvtmGaz9Itx44knG\nupIXvPYb5v/vSSRSgkrmuwPfAfOBU4Gq1trGuAS+f/7xTUBT3E6xbwJDgV8EdH8Rkbj2xYfw4czD\nn+/IgP/c7o6L/6x8w5bQuLX/1/mV2vj1FI8W/S90G4V5mT0pesuEotn+TLfg9VCW93jrrnDWVeHG\nJFIaQSXzfwZqAqdbaxdY69bZW2vzrLUfAmcAtYA/W2tXAyNxyf/ogO4vIhK3Nm+AV/9d/PihLHj5\nb9GfeFa0nEP+b2qONovawadrzYaVLrmLVmnpcMoF3mObVsOKT8ONJx7s2118g5wCNevBpb/SgleJ\nTkEl8xcCM621OV6D1tps4DXgovzP9wPvAB0Cur+ISFw6sA9efMC/PV67Hv5dWSqLlQtdG8GiEhJc\nvXxJGreG9NrFj9s8WL00mPgqSr9h/ruOzpnsOrJI6dVvBjc+AG26HXk8qQpcfptbSC0SjYJK5uvi\nauZLkpx/XoEMgtu0SkQk7uTlwbRHYfsP3uO1GsDI8ZBQyVcf+fWWb9fTO1EvLCGhhFKbKK6bB0hJ\nhdN82kls3QTL3g83nniQlg5X/QFOOf/wseE3QpM2kYtJ5GiCSubXAhcbY9K9Bo0xNYCLgW8LHW4M\n7Ajo/iIicee9aW7W2UtSFbjsN0duhFQZ7d3t30Kw58DSXcOv1Gb1EtdBKJr1OcO9qfPyzhRXgiTH\nJjERzv4JjPglDLgIegyIdEQiJQsqmX8St7j1E2PMaGNMK2NMav7HK4BPgCbAfwCMMQYYCET5Q0wR\nkchYtQjefcl//ILrNVsI8Pl873KSqmlu19fSaNvduxb6wF7YtKp88VW0pGQ4fZT32O5t8Nnb4cYT\nT3r0hzO0sk9iQCDJvLX2EeAJoBPwPPANsDf/43O4DjZP5Z8H0AB4EXg4iPuLiMSTHRnwyiP+HUn6\nnlP6Wed459fF5rhTIPloxZ/5UlKhVVfvsZVRXmoD0P1UaNDCe+y9qW6jMRGJX4HtAGut/SkwAHgW\nWIIrvVma//lAa+0Nhc7dbK39rbV2blD3FxGJB9kHXXu8gz6bQrXo5EoABDLWww/feo8day/wjjFa\nNw9uzcQZl3uP7dsDC14PNx4RCVdgyTyAtfYDa+1Ya20fa217a23v/M+1DEdE5CishRlP+G8GVb0W\njPq1K60Q/1n5uo2hecdju5Zf3fzmDbBr67FdKxI69vH/O384wyX14uzcEukIRIIVaDIvIiJlt3iu\nqwH3kpAIo35z9O4slUVurn+3lp4DwZhju17dxlC3ifdYLMzOGwNn+tR3Zx2A+dPDjSdafb8WHv0F\nzHoacj2baYvEnsCTeWNMojGmoTGmhdcr6PuJiMSD7T/A/57xHx96NbTsFF480e6bZbB3l/dYWbuP\ndPSZnV8ZI5tyteoK7Y/3HvvkDbcgtjLbt9uVsOVkw8f/g4n3uGMisS6wZN4Y080YMwvIBL7HtaEs\n+lob1P1EROJFbi5MfcR/Y6ieA+HEs0MNKer59ZZv3RVq+7RqPBq/ZP7bL9xuu7FgiE/tfM4hePeV\ncGOJJrk58NLDR76hWfclPH6rm60XiWWBJPPGmM7AAtwC2NmAAT7P//P2/M/nAS8EcT8RkXjy3lTY\ntNp7rGELOP+6Yy8biWcH9sHXn3qPlafLT4tOrrNNUYey4dvlZb9umJq0gW6nFD+emARVqvp3SIp3\nbz0P335Z/PjubfDcn9TxR2JbUDPzv8ft8HqytfaC/GPTrbVnA61xHW26AHcGdD8RkbiwYaVL5r0k\nJbsdXpNTwo0p2n25wHszpOQU6Nqv7NdNSoa2PbzHYqFFZYHTR7mdbcG9Cew5EH7xqCvVqoxvCpfM\ng49m+Y+fO9b7TZxIrPDYJqNMBgKvW2u/KHTMAFhr9xljrsfN1P8JGBPQPUVEYlrWAVdek5fnPX7m\nFW5mXo7kV2LTpW/5k7KOveGrj4sfX7kIzrWxkQzXbQK9h0DmTld2U5n/DX23BmY+4T/e/0LvJxki\nsSSoZL4eUPghcQ6QVvCJtTbHGPMucGFA9xMRiXmL34Gdm73H2vWAvkPDjScWbP8BNnztPXasveW9\ndPDpN797G2zZGDuJ8bCxkJgY6Sgia+8umPyA91MccIuFh1wWbkwiFSGoMpsdQPVCn28Div7IywZq\nBnQ/EZGYd9IwOHccJBXZqTQtHS686XCphBzm11u+Rl1ofVz5r1+9FjRt5z0WS6U2lT2RL1jwume7\n93idRjDyl67lq0isC+pXxTdAq0KfLwLOMMY0ADDGVAMuwHW0ERERXMlG37PhxgfdwsUC598ANepE\nLq5olZcHS/16y58WXGLmt4FULPSbF+eNibDuK++xKlXh8tsgtbr3uEisCSqZfxsYlJ+0AzwB1AGW\nGGNeAb4AWgITArqfiEjcaNAMxt3n6nf7DIGuJ0U6oui0fgXs8tm9s+dpwd3Hr0XlhpWwPzO4+0jF\nWDzX9dX3c9FNsVMuJVIaQSXzTwHXAqkA1tpZwPj8zy8GGgD3A48GdD8RkbiSlOwWvJ5/Q6QjiV5+\nJTbN2kP9ZsHdp3FrV25TlM2DNUuDu48Eb9NqmPkf//HTLi5fxyORaBRIMm+t/cFa+5K1dluhY48A\n9YHGQLq19g5rrU/PBhERgdjolhIJ2Qdh+QLvsfL0lveSkOC/EDZWdoMtrQP7Ih1BcDJ3wosPuHp5\nLx16weBLw41JJAwVurzKWptrrd1sbcVtU2GMucIYY/NfYyvqPiIiEjkrPvXeITcxqWJaC/rVza9e\nDHm5wd8vbJs3wKS/wr9/5d/tJZbkHIIpD8GeHd7jdRvDCC14lTgVVGvKiDDGNAf+BezlyG46IiIS\nR/x6y3fs7br/BK1dD/dGoegs74G9sHE1tOwU/D3DsHMLvPsSLH3v8G6wn70N/YZFNq7yeuNZ/5al\nPy54reY9LhLrApuZN8Y0M8Y8bIx5xxiz0hiz1uP1TYD3M7idZbfjFtyKiESljav8+8nL0e3eDms/\n9x7rOahi7pmSCq26eI/FalebD2fCIz93b4wKPy9/b6rbwCxWLZwDn77lP37xzdCgeXjxiIQtkGTe\nGDMQWIVb9Noft2GU8XgFWdZzMzAYuBqIo6o/EYkn+zPhxQfhsV8VT6KkdJa95/3frVoN6HB8xd3X\nr9QmlvrNF1a3sXc9+b49sOD18OMJwoaV8PpT/uMDR7qdgUXiWVDJ9QNAInAVUNVa29xa29rrFcTN\njDGdgb8Cj1hrfboOi4hElrUw4wnI3OFmPv/7T3j5b2pveCysdSUhXrr3d6UwFaWjzyLYzeth19aK\nu29F6dgHWnT0HvtwhkvqY0nmTpjyoP+C1459YNAl4cYkEglBJfPdgBettf9X0R1rjDFJwAvABuCO\nMl5jkdcLiNEqSBGJRkveha8+PvLY8gXw2C3w7ZeRiSnWfPcNbN3kPRZ0F5ui6jZxLy+rYrCrjTFw\nxmjvsawD8P5/w42nvKpUhRY+v7XrNYERv9AuylI5BPXPfCfgs4Y8cHcCxwNjrLUxXOUnIvFsRwbM\netp7bM8OyI2DDiJhWPqu9/EGLVw/+IrmNzsfq3XzrbpCe5/SpE/fhN3bvMeiUUoqXPorOOOKI1u6\npqTB5bdD1bTIxSYSpqCS+deBAPff82aM6YubjX/YWvtRWa9jre3t9QJ81sKLiJRebi5MfdS7lSK4\nziHteoYbUyzKOQSff+A9dvzAcHry+9XNr/0CDmVV/P0rwpDLvY/nHIJ3Xw43lvIyBgZcCFf+Dqrm\nd6sZcTPUbxrZuETCFFQyfwdQ0xjzmDGmQpo/5ZfXPI9baPuHiriHiEgQ3v8vbFzpPdaguX+pgxxp\n1WLXCrIokwA9BoQTQ8vObga4qEPZ8O3ycGIIWpM2/r35F7/rX9YUzdofDzfcD+dfD51OiHQ0IuEK\nagfYbcDZwCggI78Gfa7H651y3KY60AHoDBwstFGUBf6Yf85T+cf+Ua6/kIhIGW1cBfN8ZjcTk2Dk\nLyE5JdyYYtUSnxKbdj0gvXY4MSQlQ9se3mOxvBvs6aO868ltHrwzJfx4glC3MZxwZqSjEAlfIH0A\njDFdgXeBgh+vfs3CytOULQvwqUClV/49PwBWAmUuwYmkuS9Dei03E1SvqRbuiMSarAMw9RHI82kD\ncMZoaNQq1JBi1r7d/otMK3rha1EdexdfyAyubt6ODafcJ2h1m0DvIW7DqKK+/Ai+WwNN24Ufl4gc\nu6Caev0NqItbnPoc8L21NtANr/MXu471GjPG3IVL5p+z1k4I8r5hOZQF70873GIrtbpbpd+ys9tp\nsElbN0MkItHrjYlu4auXNt2g37mhhhPTPv8A8jx+i1RNg84hl1H4LRjdtRW2bISGLcKNJygDR7q9\nD3Kyi4/Nngxj7gw9JBEpg6CS+X7Af6219wZ0vUrnuzVH9so9sBdWLnQvcIl803aHk/vmHV3CLyLR\n4atPYNEc77HU6nDRz/W07Vj49ZY/7uTwy5TSa0PTtq5NZlErF8VuMl+jDpw0FD54tfjYN8vcIt82\n3cKPq6g1y9zkVhWVp4l4CiqZzwbWBXStSmn9Ufro5ByC9Svcq0CDFi6xL0jwa9aPzce9IrEucyfM\neNx//PzroWbd8OKJdZs3wPceiTOEX2JToEMf72R+1SLXTSVW9R8OC9+Gg/uLj82eBNf9JbK/V9Z9\nBS/82f2+G30r1GoQuVhEolVQyfw84MSArnXMrLV3AXdF6v5BKJykl9aWDe5VUPNYo+7h5L5FZ2jY\nHBISg41TRI5kLfz3X/67uh4/0M0mS+ktned9vE4j/02CKlrHXvDuS8WPb1zp/t+npYcfUxDS0uHU\n4TBncvGxTathxafQpW/4cQHs3g5THnLlVhnfwuO3waW3RMfTApFoEtRD31uBLsaY243R3PCxysv1\nb2N3LPZshy8+hNcnwL9/BfeNKXm2UETK75M3YM1S77HaDWDoteHGE+tyc2HZ+95jPU+L3Cxx4zZQ\nvVbx43l5/v//Y0W/Yd5/N3BJvtfahYp2KBtefMAthC6wfw88dw8seN29iRYRJ6hk/vfAcuDPwBpj\nzDRjzDMeL79uNJWaxW073X+4m3VKDOh5SdZ+94tRRCrG5g3w1gveYyYBLv6FdqE8Vms/d2VLXnpW\n+NaE/hISoIPPbrCx3KISoEpVGDjCe2zrJv/1CxXFWnjtSbeWrKi8PLex1d5d4cYkEs2CKrMZU+jP\nrfNfXiygeaoiEhOhYx/3Ajcj8f03+TXyX7tZe6+NU0qjZSkfSa9cBPUaQ53GqrsXKY2cQ64NpVcn\nEIDTLir9958ctmSe9/FWXaB2w1BDKaZDb1g8t/jxNUvc7HUslzX2HgIfzoSdW4qPzX0JuvcPr6Pa\nJ2/47zFgDFwyPrx9BkRiQVDJvF/yLmWQXCV/UWtn93lenpsd2fD14QR/l8cPXC8tOh/9nJxDri4x\nJxuq1Tyy7r5xq+CeFIjEk3dehIx13mPN2ru2f3JsDu5zNdpeIrXwtbB2PdzPw8Kdx8DVzG9aHbl6\n/iAkJcPgUTDt0eJju7fBZ2+F01r12y/hjWf9x4dc7t8qVKSyCiRNs9auD+I64i0hwbU+a9ji8O52\nu7cfTu43fA0Z693OfYWl1YB6TY5+/e/XHp5d3Lfbtdj76hP3eXIKNO9wuOd98w7eW5uLVCZrv3Cz\nmF6SU2DEzXoTXBbLP/J+0pFcBbr2Cz+eolJS3c/BtV8UH1u5KLaTeYDup7o2lZs3HHncJMCeHRV/\n/11b4aWH/Ddd69oP+sdw5yCRiqJfNzGqZl3odop7gWsrtmnV4Zn7TavcDHtpSmY2lNBJ51CW+8VV\n8MvLJECjlvkz9/kJfo065f/7iMSSuS/5L8AberXbXVOOnV8Xm84nRc/ag469/ZP5M0aHH0+QEhLd\nzPekvx4+1rUfnD4K6jer2HsfyoIXH4R9e7zHG7aAi25SGaiIFyXzcaJqGrTr6V7gHgMf2Fe6rz1a\nj/vCbB788K17ffw/d6x2A1eSU1CeU6+pNseR+HbFb+H1p2FZkYWBnU5wtcdy7HZk+LfoPT6CC1+L\n6tjb7fRb1Ob1rhylZr3QQwpUxz7QoiMkV4UzLnebFVY0a2HGf/z3FkitDpff5hbqikhxSubjVGIS\nVK959POsdWU65bFzi3sVJDap1d2sfacToI8SG4lDVau5UpqOvWHmf1ytd/VaMPynmjksK7+OKTXq\nRFdf8bpNoG5j2P5D8bFViw+XQsYqY+CqP4RbTvnxrOJvjH+MJ8EteK3TKLx4RGKN5k8ruUPZcPwg\nt2AvqE4MB/bCyoXw7fJgricSrbqdAjf9zSWbF90E1WpEOqLYlJfnX2LTY0D0dYnp0Nv7+MqF4cZR\nUcJM5Nd+AW8+5z9+5ujDT5xFxJtm5iu5Kilw9k/cn7Oz4LvVruxm/QrXEjPrQNmvXdq2fHt3uyRI\nM5oSi2rWgzF/1L/f8tjwtXdLRIiOLjZFdewFH71e/PjaL1ztd3JK+DHFol1b4KW/+S947XYKnHJB\nuDGJxCIl8/KjKinQ+jj3Atc3efOGw4tq16+AzGPoaFCatpi5OfD3n7qyhRadDtfdN2wRfbNxIn6U\nyJeP36x807bQoHmooZRKyy6ufjv74JHHD2W71op+m0vJYdlZMPkBt6url0atVLYmUlplSuaNMTuA\nv1prH8j//E5gnrXWZxNuiUUJidC4tXudNNTV1+/aergd5vqvYcsG76+tmla6X8IZ69wvxOyDsPxD\n9wL3mLd5x/x++52gaXv3ZkNE4kt2Fixf4D0WjbPy4Hqyt+txuIVvYasWKZk/GmthxuOukYKX1Opw\n+a1a8CpSWmWdma8FFP42uyv/pWQ+jhnjOtfUbnB4W/X9mbBx1eEEf9NqN9veolPpOtr4Lb7NOgBr\nlroXuDcWTdocbofZolPpFviKSHRb8al3OV9iktt1NFp16O2dzK9cBMPGVo4Z5dycsu2nsOA1+Hy+\n95hJgEtvifxuvyKxpKzJ/GaggrvOSixIS3cdPTrmLwg7lO02oSrtL7LStsXMy3VvFDatdr8IwHWV\naFkoua/buHL8ApXwLF/gWvUlV4l0JPHLr8SmQ2/38yVa+c2+79oKWza6UsF4tf0HeGcKHMiEn9x5\nbF/7zTJ46wX/8bOuhLY9yhefSGVT1mT+Y+BKY0wuUNCga6A5eiZlrbV/KuM9JQYkVyn9wldr/ftK\nl8b2791r8Vz3ebUah/vdt+nmyoNEymrFZ/DSw9CgBYz8havhlWDt2QHffO491jOKest7Sa/tavq/\n8+iNvmpxfCbze3bAvFdg0TtuggXc/7+23Uv39fsz3YLXoruVF+g+AE4+L5hYRSqTsibzvwE6ANcX\nOjYw/1USCyiZF8D15k6v7Xb88/vhfiz27YEVn7jXcSfDpb8q/zWlcsrcCa/+2/15ywZ44ja3u2e/\nc7UhWpCWve/9vZ+WHht15x16+yTzi6D/8PDjqUjvTYP3prqnr4XNmewmT0rzVDQtHc68El5/ypXo\nFNaoNVxwg56uipRFmZJ5a+0aY0w3oDXQFJgHTARK6BYrcqTU6vDTh+Dgfti0ypXcbPja1eAfyirf\ntVuU8umASFHWwvTHjuyykZvjemGvWgwjf+k2iJLysRaWvOs91r2/W2Qa7Tr0hndfLn58w9duv43U\n6uHHVJGKJvLgSh9XfApd+pbuGn2GuKcWLz7g3jQDpNWA0beqyYFIWZW5NaW1Ng/4Bvgmv7xmnbXW\nZw83EX9V09ymIAUbg+TmwA/rYMOKw20x9+0+tmu2LEVbTBEvn74Jq5d4j+3IiI0kMxZ8vxa2bvIe\ni9YuNkU1aePe2O3ddeTxvDxYvRS6nxqZuCpCv2Hw8f+K/13Bzc536lP6dsLNO8CND8KLD7q9TS69\nBWo1CDZekcokkD7z1lo9eJbAJCZBs3budfJ5bgZvxw+He92v/9rVyvupUhUatgwvXokfWzbBm897\nj5kEuPhmtyeClJ/fwtf6zVySHAsSElw5UMG6ncJWLYqvZL5KVRg40pXIFLV1Eyx9D3oNLv310mvD\nNXe7n+ltugUXp0hlFPimUcaYZsDxuPaVu4HF1lqf+ReRozPGda6p2+TwL4u9u92j7A35Cf73aw8v\nyGreERK14ZQco5xDMPUfkONRSgCuBrpVl3Bjilc5h/xbEx4/KLbqpv2S+dVL3M+keNr8rvfp8OEM\n791657507OVRScmlXzwrIv4CS+aNMS2B/wBneIzNBm6w1q4L6n5SuVWv6Wo0C+o0s7Pc49r1X5e+\nP3HmTvemoHGrCgtTYsjcKf6b2DRtC4MvDTeeeLZqsetsUpRJgB4Dwo+nPNr2cE8Tiy7o3J/p6snj\naf1OUjKcfhlMfaT42O5t8NlbbpG4iIQrkPIYY0wj4APgTGA98ALwQP7HdfnHP8g/TyRwVVKg9XEw\ncP4Z3i0AACAASURBVAT0KMVGMwf3wfP3wtN/gG+XV3x8Et2+XQ4fzPAeS06Bi39Rts1xxNtSn9VV\nbbtDjTrhxlJeVdP81+isXBRuLGHodqp/281507w3ABORihVUrfsfcF1tbgPaW2vHWGt/a60dg2th\neSvQBPh9QPcTKbND2TDpr5CxDrL2u6TeaydHqRwO7INp/3RrM7ycMwbqNw01pLi2P9PVk3uJlYWv\nRRVsmlfUqsXhxhGGhATXptXL/j1uU7+Fs2HzhnDjEqnMgkrmhwFvW2sftNbmFh6w1uZaax8C3gb0\nAE4iKjcXXvk7rPvq8LGcQzDlIVg4J3JxSeS89qQrEfDSsQ/0KVY4KOXx+fziJSkAKanQ+cTw4wlC\nB59kPmOd/7+tWNaht3/50PzpMPM/8ORv4cuPw41LpLIKKplvBBztgeKi/PNEIub1J11P5KJsHsx4\n3G2M4jdDK/Fn2Xz44gPvsWo1YfhPY2sxZizw62Jz3Mmx22e8XhOo4/PbLR5n543xn50/lO1+hmYf\nhCkPuraVeQFsCigi/oJK5ncDR2sG2CL/PJGIaXVcyd0l5kyG/z2jXz6Vwa4tblbez0U/cwutJThb\nNnrvmAqxW2JTwK/UJh7r5sF1dirNLr3vTYNJf3HlbCJSMYJK5j8ARhhjTvYaNMb0BUbmnycSMT36\nwxW/dYsa/Xz8P9etIedQeHFJuPJyYeqjbs2ElxPP9i+dkLLzm5Wv3SD2u774JfNrvyj/jtbRasjl\npTvvu2/cTL2IVIygkvk/5398zxjzgjHmGmPMOcaYq40xzwEFHYXvC+h+ImXW/ni4+q6St1r/4gO3\nSFadGeLTBzPc/gRe6jWFs64KN57KIC8Xlr7vPdZzoFtYGctadnEbKxV1KAu+/TL8eMLQuLXrblOS\nhEQY9WuoWTecmEQqo0B+fFprFwMjgD3AaOAp4HVgAnBl/vFLrLVx+sBRYk3zDjD2z1CjhF8wa5bC\nxLth357w4pKK99038M4U77HEJBj5y9it3Y5ma7+AzB3eYz1PCzeWipCU7HrOe/Hr3hMPTh9Vcuni\nsGu02ZpIRQtsLsRa+zquLv4K4O/AM/kfrwRaWmtnBnUvkSA0aAbX3ee2j/ezaTVM+B3s2hpeXFJx\nsrPcLq95ud7jg0dBkzbhxlRZLPHpLd+ys//i0VjT0aeGfOXi+F1YX7ex2xnWS+/T4YSzwo1HpDIK\n9MGmtXaftXaytfbX1tpx+R8nWWu19EWiUs16MPZeaNbe/5xt38NTv3OL9yS2vfWc+//ppVUXOPX8\ncOOpLA7uhxU+bQpjfeFrYX7rLHZtga2bwo0lTEMudx19CmvTDc4dp25QImGI8SpFkfJLS3c19O16\n+p+zZztM+D1sWBlaWBKwXVth8VzvsappcPHNJZcLSNl9+ZFrWVhUUhU4rl/48VSU9NrQpK33WLx2\ntQH3M/Sae9yTre4DYOg1cNUfXOmRiFQ8JfMiuIVro2+H7v39zzmwFybeFZ99oyuDWvXh+vuhgcdW\n9Ode58alYvh1sel8IlStFmooFc6vXWM8182DeyMzaCSM/AX0GwaJemMsEhol8yL5kpLd7Gy/Yf7n\nHMp2XW6W+tT/SnRr1BJuuB9OLrQXdff+rmWpVIydm4/ccbmw4weGGkoo/FpUbvjaTQiIiARNybxI\nIQkJcM7VMMRnd0NwiyenPQoLXgsvLglOchX3//gnd7quRueOi3RE8c3vjW96bWjbPdxYwtCkrds9\nuKi8PFizLPx4RCT+KZkXKcIYOO0iuOBGMCV8h7wxETauCi0sCVi7HjDuPkiNszKPaGKtfzLfY0B8\nrlFISPAvtVm5MNxYRKRyUDIv4qPPELfZid8irkGXupldiV3qtFGxNnwNOzK8x+Kpi01Rfl1tVi/x\nb4sqIlJWSuZFStClr+vKkJJ25PETz3aLvUTEn9/C1yZtoaHHQuR40a6H91OH/ZmwaU348YhIfKuQ\nZN4YU98Yc5cx5pX81x+NMeoVITGpdVe49h6oXst93rWf29VQs7oi/g5lwRcLvMfiYcfXklRN89/1\nNN672ohI+AJP5o0xJwNrgD8AA4AzgT8Cq40xJwV9P5EwNG4N4/7sdjQc8Yv4rPWNFwf3wZc+GxRJ\neFZ8Bln7ix9PSITup4YfT9h86+aVzItIwCpiZv5RYDHQylrb0FpbExgEZAN/r4D7iYSiTiMY/lNt\nhBLtXn8apjwIUx9xib1Ehl+JTYde3t1e4o1f3XzGOti9PdRQRCTOlTmZN8YM9RnqAdxrrd1YcMBa\n+x7/z959h1lVXf8ff++p9N57nUEBGYoNlKLYe4u9m96/MT1GjOmaX3pMosYSsfeuqCAqikpvDh2k\n9zZMn/37Y1/CMHPOtHvuue3zep77DHP24e4VA8O6+6y9NkwF6jhjUyS1VFXFO4L0s+gDWBDpnrJg\nJvz9e/49ziV29u/2b8M4clK4scRLpx5uAcCLSm1EJEjRrMy/bIx5yBjTvsb17cDY6heMMRnA8ZEx\nkZS3d6dLJFcvinck6WPvDnjxX0de27Md/nM7TJvq2iRKOBbMBOvxYbZ5K//yk1RjjP8BUjpFWkSC\nFE0yfzpwMrDEGHNRtev/Bu4wxrxhjPmtMeaPwBLgBOCfUcwnkhQO7oeH74Rt6+HhX6p+OwxVVfDM\nX73LamyVO3lTG5bDYS3Mm+E9dsxJ6VWm5ldqs2qhO01aRCQITU7mrbVvAcOBZ4GnjTFPGGM6AXcA\n/wcMBX4AfBtoBXzbWvvr6EMWSVxlpfDIb2BbpMissgKe+AN88mZ840p1s16ENYu9xzr2gDOvDzee\ndLZ5jfsg66UgTUpsDul3NOQ0q329vBTWLgk/HhFJTVFtgLXWFllrvwFMxNXDLwOusNb+yVrbC2gL\ntLXW9rbW/jXqaEUSWGUFPHE3fF545HVb5co/ZjytUo9Y2LwG3nrMeywjEy77tndCJbHht/G1cy/o\nOTDUUOIuKxsGjvAeU1cbEQlKIN1srLXv4Ta+PgQ8bIx5wRjTzVq731q7P4g5RBLdhpX+m/4A3n4M\nXrlfG2ODVF4KT/3JfZDycsrl0HNQuDGls8oKWPie91jBhPQsdcr32SOwfI4+3ItIMAJrTWmtLbHW\n3gqMAwYBS40xNwb1/iKJru8QuObHkJ3rf8/s1+DpP0FFeXhxpbI3H4HtG7zH+h4FJ18Ybjzpbvk8\nKNpX+7oxMCLFD4ry41c3v3ub/59dEZHGiCqZN8Z0Ncbcaoz5a+Rrd2vtx7iSm38A/zLGvG6M6R1I\ntCIJbvBIuHEKtGjtf8+iD2Dqb6C0OLSwUtKKefDRq95juS3gkm/pcK+wzZ/ufX3AMdC2Y7ixJIrW\n7aHHAO8xtagUkSBE02d+JPAZ8Hvg65GvS4wxo6y15dbanwHHAV2BxcaYrwYRsEii650HN/8S2nby\nv2flAnjwDu9VTKlf0V549m/+4+feAu27hBePuC5OfnXgIyeGGkrC8VudL1SLShEJQDQr838AKnGb\nX1sAEyLf333oBmvtfGAMcBfwR2OMz7qNSGrp0gu++Cu36c/PhhVw309dL3RpOGvh+XvgwB7v8WHj\nYMT4cGMS98TJa+9CTjM46rjw40kkfv3m1y+DYp1SLCJRiiaZHwU8bK2dGamXfw/4b+T6/1hrK621\nv4xcV08JSRttO8Etv3Qr9X52bIJ7f3K4laXUb85b8Nkn3mNtOsL5X0rPjZbx5tfFZthYdRPqMRBa\ntq19vaoKVs4PPx4RSS3RJPO7gJ41rvWMXK/FWrsUtzlWJG20aA033O5q6f3s2wX3/QzWF/rfI87O\nTfDqA95jxsAl33SnjEq4tm9wT5q8FEwMNZSElJEBeT4/A9SiUkSiFU0y/yhwmTHmXmPMl4wx/wIu\nBXw6PoO1Xgd8i6S2nGZw9Y/qLv0oPgAPTtEx73WprICn/uzaUXoZdz4MGB5uTOLMf9f7ersurquQ\nQN4Y7+sr5kFVZbixiEhqiSaZvwP4E3Al8E/gGuAvwJTowxJJLZlZcPE3Yey5/veUl8HU3/onRulu\n+lOwcaX3WLf+cOqV4cYjTlWl/5/ZgvFuVVpg0DHe3ZUO7oONq8KPR0RSR5N/zEY61vyftbYVrmNN\nK2vtd6216qAt4iEjA868AU67xv+eqkp45i8w66XQwkoK6z6Dmc96j2XluFNes7LDjUmcNYth307v\nMZXYHNaspf9TisJPw41FRFJLUCfAbrdWZ9mJ1McYGH8RXPhVMHX87XvtQZg2NbSwElrJQXj6z+BX\npHfGtdBFJ1nEzbwZ3tf7DIGO3UMNJeH5dbVRi0oRiYYegIrEwejJcOX3615N7tA1vHgS2cKZsGeb\n99jgkXD8WeHGI4eVFsPS2d5j6d5b3otfv/kta/yfboiI1EfJvEicHHUcXH8bNGtRe2zy1S7hFzj2\nDNelJrf5kddbtIGLvq42lPG05EPvDclZ2TB0bPjxJLpOPaBDN+8xdbURkaZSMi8SR/2Gwk13Qqt2\nh6+deK4rxRHHGFd7/fX/d2TN8YVfhdbt4xaW4N9bfshx0LxlqKEkBWMgb5T3mDpZiUhTKZkXibPu\n/dxpsR26wTHj4czrtdrspX0XuOkOmHwVHHemThWNt93bYM0S7zGV2Pjzq5tftdB1tBIRaayseAcg\nIi6R/9JvXCmJWvn5y8iECZeAttvH34KZ3tdbtYOBI8KNJZn0G+rOnigrOfJ6eSmsXVL3AXMiIl6U\nNogkiJZt1F6xofTkIr6s9S+xGTEeMj36qYuTlQ0Dj/EeU928iDRFTJN5Y0xHY8xFxpgzjDH68S4S\nkK3rYfWieEch6erzQti52XtMveXr59fVZvkcPXUSkcYLJJk3xnzVGDPbGNOh2rXRwGfA08CrwCxj\njLZEiURpzzZ4+E54+Jeum4hI2Px6y3fvD936hhpKUvLbBLt7G2zfGG4sIpL8glqZvxyw1tpd1a7d\nBbQHHsAl88cCXwloPpG0VLQPHroT9u2Cygp44g/wyZvxjioY65bBtg3xjkLqU14Giz/wHtPG14Zp\n0wF6DPAeW65SGxFppKCS+cHAwkPfGGM6AROA+621t1hrzwM+Aa4KaD6RtFNaDP/9FezYdPiatfDi\nv2D6U8n9eL5oHzx+N9zzfZj9WnL/b0l1n33iTuWtKSMThp8cfjzJym91XnXzItJYQSXzHYHqZzSO\ni3x9rtq19wA9gBVpAmvhqT/CxpXe4+88Dq/cD1VV4cYVBGvhhX/CgT1QUQYv3+c+tOzfHe/IxIvf\nxtfBI6FV21BDSWp+dfPrl0FxUbixiEhyCyqZ3wV0qvb9BKAKmFXtmgWaBTSfSFoxBk4427W08zP7\nNXj6T1BRHl5cQZj7DiybfeS1FfPgb//nvkri2L8bVs73HlOJTeP0HOQ6WNVUVeX/31hExEtQyfwy\n4LxI95p2wBXAJ9bafdXu6QdsCWg+kbQzqABunAItWvvfs+gDeOQ3riQnGezcDK/+x3vs4D713E80\nC9/zfvrTvBXkjwk/nmSWkaHTYEUkGEH9U/lnoDuwAfgc6Ar8o8Y9JwALop3IGPM7Y8zbxpjPjTHF\nxphdxph5xpjbjTEdo31/kUTWazDc8kto28n/nlUL4IEpULQ3tLCapLISnv5z7cNzDhl7ng4fSjR+\nXWyGj9MZCU3h26JyLlRVhhuLiCSvQJJ5a+2LuE41S4BC4FZr7SOHxo0xE4FWwBsBTPddoCUwDfch\nYipQAUwBFhpjegcwh0jC6twLvvhr99XPxpVw389cG8tE9e7TsGGF91jXPjBZ2+UTyuY1sHWd95h6\nyzfNoBFu43BNB/fBxlXhxyMiySmwh9jW2n9ba8dEXn+sMTbDWtveWvvvAKZqY609wVp7k7X2R9ba\nb1prjwV+DfQAfhzAHCIJrW1Ht0LfO9//nh2b4N6fugOmEs36QpfMe8nKhsu+C9k54cYkdfNble/U\nwz0xksZr1hL6DvEeU1cbEWmopKtItdb6PJTnychX/bMiaaFFa7jhdv+6W3D96O+/DdZ/Fl5c9Skt\nduU1fp13Tr/GrcxL4qiscPXyXgomuQ3a0jR+ew3Ub14OqSh3BwTOeimxfpZL4gg8mTfGZBpjuhpj\n+ni9gp6vmvMiXxfWeZdICsnJhat+CCPG+99TfAAevCNxVvpe/Q/s3uo9NnAEHH92uPFI/VbO996D\nYQwU1PFnT+rn92F88xrYtzPcWCTxlJe6PVCP3w2vPeietiZrG2KJnayg3sgYMxz4LTAJyPW5zQY1\npzHmVlwdfltgDHASLpH/bRDvL5IsMrPg4m9Cy7Zu5cZLeRk8+lu48OvxbSG45CPXitJL81Zw8TfU\nwSYR+ZXY9B9W92ZsqV+nntC+q/cH3OVzYcxp4cckiWP6k7VX4z96FWwVnHOLnoqJE1RifRSHe8pP\nw62SLwC2AqNwPeinA0FW796K65pzyOvADdba7Q2I12+N0qd6USSxZWTAmddDq3bw5n+976mqgmf/\n6jbXjTs/3PjArTK+cI//+AVfdcfcS2I5uN+d+upFveWjZwzkj3YJWk2Fc5TMp7N9O+FDjz8XALNf\nh8xs93NfCb0EtQb2MyAbGGutvSBy7Tlr7ZlAf+AB4Gjg5wHNh7W2m7XWAN2Ai4EBwDxjTB0VxCKp\nyxg4+UK48Gtg6vib/fpD8MbD7uTVsFRVwbN/dyU/XkadAkNPCC8eabjFs1zNfE05zeBo/X8WiHyf\nFpWrF7mnapKepj/lTsX2M+slePux8OKRxBVUMj8ReNlau6jaNQNgrS0CvgzsBu4MaL7/sdZutdY+\nB5wOdAQebsDvGe31ArS1RJLe6FPhyh9AVh3dYGa95Gpyw/LRq67/vZcO3eDsm8KLRRpn/gzv60NP\nqPtEYmm4fkO9/1uWlcDaJeHHI/G3fSPMfbv++959Bmb4dAaT9BFUMt8JqN4xugJocegba20Frszm\n9IDmq8Vauw5YCgw1xqiKU9LaUcfC9T+DZi28xy/8OvQYEE4sW9bBtEe8xzIy4NJvQ27zcGKRxtm+\nET5f7j2m3vLBycqGAcO9x3QabHp6+7GGb3J9+zF4/4XYxiOJLahkfhduM+ohO4CanWvKcJtVY6lH\n5KvOzpO0128o3Hynq6Ov7szrw6t1Li+Dp//kWqt5mXAZ9M4LJxZpPL9V+Xad3Z8vCY5fqU3hnHBL\n4iT+Nqx0rSgb442HvfddSHoIKplfBfSr9v0c4DRjTBcAY0xL4AIgqgf7xpg8Y0ytDwTGmAxjzK+A\nLsAsa+3uaOYRSRXd+sGXfu1KWQBOujDcza9vPep/aFXvPJhwSXixSONUVcGCmd5jIyao61DQ/FpU\n7t4KOzaGG4vEj7X+TzKzcqB9F//f+8r98OlbsYlLEltQP47fBCZFknaAfwIdcBtSnwIWAX2B+6Kc\n52xgizFmmjHm38aY3xhj/oMr8fkJsAX4YpRziKSU9l3hi7+CU690BzKFZdUC/1aZOc1ceU2mx1H2\nkhjWLoG9O7zHCiaEG0s6aNMRuvf3HkuUMyIk9lYtdBufvZxwNtx0R93tYF/8p/8TNUldQSXz9wI3\nA80BrLWvAN+NfH8JbsX8d8BfopznLeB+oDOug833I++/C7gDGGqtXRrlHCIpp1U7mHhpuC3MZj7n\nP3bOzYefFkhi8ust3zsfOvXwHpPo5PmU2ug02PRQVeW/Kt+spetW1q4L3DgFWvu08bXWdQ5b9EHM\nwpQEFEgyb63dbK19wlq7o9q1P+OS7u5Aa2vtT6y1UZ1ZZq1dbK39hrW2wFrbyVqbZa1ta6091lo7\nxVq7K8r/KSIClBZH/x5X/xiOO7P29aOPh5GTon9/iZ3SYlj6kfeYesvHjl/d/LrPoKQo3FgkfEs+\nhE2rvcdOvhBatHa/7tgdbrzdHRToxVbB03+GZR/HJk5JPDGterTWVkZaR2r7jkiS2LwW/vg11188\nGjm5cN4X4ZqfHP5Hp3V7dziUDjlJbEtnu7aINWVlw7Cx4ceTLnoOhJZtal+vqoSVPq1dJTVUVrg9\nRl5ad4ATzjnyWudecMPt7uRsL1WV8MQf1A0pXWgLk4j8z64t8PCdULQPnvx/8PHr0b9n/mj4xh9h\nyLFw8TcOry5J4vKruc0f4588SPQyMmGwz0ZY1c2ntjlvuZ+/XiZd5hZHaurW1yX0fi2IKyvgsbv8\na/AldSiZFxEADuyBh+50X8HVXr50L7zzZPSt8Vq1hat/BIMKoo9TYmvPdliz2HtM5VGx59fVZsXc\nhvcdl+RSVuJOe/XSsQeMOtX/9/YYANfd5n+AW0UZPPIbWLcs+jglcSmZFxFKiuChX3qvDE1/Al6+\nzz22ldQ3/13vD2+t2unDWBgGFXi3/SzaBxtXhh+PxN6HrxxeRKlp8lX1d/3qnQfX/hSyPVbvAcpL\n4b+/8j8ATpKfknkRYftG2LXZf/zj1+GpOg5/ktRgrUvmvRxzslqJhqF5S+h7lPeYSm1Sz8H98N7z\n3mM9B8LQExr2Pv2Odk8/s7K9x0uL4eFf+m+wleSmZF5E6J3n2p3VVc++eBY88utgOt1IYtqwAnZu\n8h4rmBhqKGlNLSrTx8xnofSg99hp1zSuWcDAY+DKH0Bmlvd4SRE89Av/g/wkeSmZFxEAeg2GW35Z\n94EkqxbCA7dD0d7D1+bNUIKfKuZN977erR907xdmJOnNL5nfvAb2qQFzytizHWa/5j02cIRLzhsr\nbxR84f/8T2g+uB8emALbNzT+vSVxKZkXkf/p3Au++Gvo0tv/no2r4N6fwe5troXhs3+Ff9yqesxk\nV17mf9CMesuHq3NPaN/Fe0ytBlPH9Cf9SxejOa376OPh0u+A8cnwivbCA3f4d8+R5OPzMKZpjDFj\ngOOA9oBXdaW11t4Z5JwiEqy2HeHmO10HhM8Lve/ZuQnu/SlURv4h2rUF7vspTLgMJlyi2upkVDjH\n+2CijAxXLy/hMca1Af3o1dpjhZ/CmMnhxyTB2va5/ynLw8a5LjXRGD7O/Xx+9m/eG9r374L/3A63\n3OlOlZXkFkgyb4xpAzwLTALqqvCygJJ5kQTXorXrX/zE3f4rgftrPO6vqnKdb1bOg8tvdR8KJHnM\n9ymxGTTSdbKRcOWN8k7mVy9yq7l+Gx0lObz1qDuptaaMTJh8RTBzFEx0f1Ze+Kf3+N4dboX+5l9A\nG/28TmpBldncBZwCvA/cBJyGS+xrvk4JaD4RibGcXLjqhzBiQuN+34E9kNs8NjFJbBzYAyvmeY+p\nxCY++g31bjVYVgJrl4QfjwRnfSEs+9h7bPSprrd8UMacBufc7D++a4urofdrjSnJIagymwuAucAk\na70+a4pIMsrMcqe2tmoLH7xY//0mAy79tv+JhJKYFr7nfSBRs5au3EPCl53jNkB+9kntscI56vmf\nrKyFaY94j2XnwqQvBD/nCWe7Ffo3HvYe37HJrdDfdAe0bBP8/BJ7Qa3MtwWmK5EXST0ZGXDm9XDG\ntfXfO+Fi6DMk9jFJsOb59JYfPs4llRIf+T5dbQrnRH8qs8THinmwdqn32InnQOv2sZn3pAvglDrK\nd7atd20riz32zUjiCyqZXwF0Dei9RCQBnXQhXPR1/5ZnvQbDxMvCjUmit2UtbFnjPabe8vGVN8r7\n+u6tsGNjuLFI9KqqYNpU77HmreDkC2M7/8RLYfzF/uOb18DDd6rVcDIKKpn/O3CeMaZnQO8nIglo\n1ClwxQ8gq8ZqbfNWcOm3/A8rkcTl11GjY3d3mJjET5uO0K2/95haVCafRe+7D89exl/sytpiyRiY\nfBWMPc//ng0r4L+/cnszJHkElcy/BrwJfGCMudEYc4wxpo/XK6D5RCROjjoWvvwbOOo41wt7UAF8\n5XfBbtqScFRWunp5LwUTG3f6pMRGvs/qfKFOg00qFeXw9mPeY206wvFnhROHMa5s8rgz/e9Ztwym\n/hbKS8OJSaIX1DraWlzbSQPcV8d9NsA5RSROuvVznW4kua2a79/FoqCRXYwkNvLHwLvP1L6+bpk7\nFyDWq7kSjE+nuYP2vJxyebh7U4xxHW4qymDuO973rF4Ej93lfs6rDWriCyqxfhiXqIuISJLwK7Hp\nPwzadQ41FPHRcyC0aAMH9x15vaoSVi6AYWPjE5c0XGkxzHjKe6xzr/jsTcnIgAu+AhUVsHCm9z0r\n5sGT/w8u/55KKBNdIP/3WGtvCOJ9REQkHMUHvNsegnrLJ5KMTBg8EhZ4dBxaPkfJfDKY9RIU7fMe\nm3xV/E7Mzsh0rYcry2HJh973LPsYnv4zXPodneydyIKqmRcRkSSyeJar460ppxkcfUL48Yg/vxaV\ny+d6nw8giaNoL7z/gvdY7zy39yieMjPd2SB1nSexeBY893f9WUtkgSfzxphexpjzjDHXGmPON8b0\nCnoOERGJzvwZ3tePPkEn+CaaQQXeLWGL9sHGleHHIw337jP+nWFOuyYxNplnZcMVt9Z9ENmCd+Gl\nf+l8g0QVWDJvjOlrjHkdWAc8DzwIPAesM8a8bozpF9RcIiLSdDs3uSPlvWjja+Jp3hL6HOU9tlxd\nbRLW7m3w8RveY4NHQv+h4cZTl6xsuPIHdcf06Vvw6n+U0CeiQJJ5Y0w34H3gdFwy/1/g95GvayPX\n34/cJyIiceR34mvbTm7zqyQevxaV6jefuN55Aioral83xq3KJ5qcXLj6x9An3/+ej16FN/+rhD7R\nBLUyfxvQE/ghMNhae4O19seRjbF5wA+AHsDPAppPRESaoKrKezMlwIjx/if8Snzl+dTNb1oN+3aF\nG4vUb8s6/79nw0+G7v1CDafBcpvDtT+FnoP873n/BfdBRRJHUD+2zwHetNbeZa2trD5gra201t6N\nO1Tq3IDmExGRJli3FPZs9x6LR4s8aZjOvdwhbV60Op943prqvXqdmQWnXh5+PI3RrCVc9zN3noif\nGU95n38g8RFUMt8NqK9yb07kPhERiRO/3vK9BkPnnqGGIo1gjP/qvOrmE8u6Zf4n9I45DTok3m9f\nFQAAIABJREFUQSbUojXc8HP3IdLPW4+6tpsSf0El83uBvvXc0ydyn4iIxEFZiX8/afWWT3x+LSpX\nLfRuMyrhs9bVlHvJaQYTLw03nmi0bAs3ToGOPfzvee1BmP16WBGJn6CS+feBS40xnsdXGGOOBy6L\n3CciInGwdLZ3m7zMLBg2Lvx4pHH6DYXs3NrXy0pg7dLw45HaCj/17xQ19jxo1S7ceKLVur1L6P1K\nvABevhfmvB1aSOIhqGT+V5Gv7xpj/muMuckYc5Yx5kZjzEPAe5HxXwc0n4iINJJfb/khx7rH6pLY\nsnNg4DHeY4WfhhuL1FZVCdOmeo+1aAPjzg83nqC07Qg33uG6Xfl54R5YMDO8mORIgSTz1tq5wKXA\nPuBq4F7gZeA+4NrI9S9Ya1XZJyISB3t3wOpF3mPqLZ888upoUal2gfG1YCZs+9x7bMIl0KxFuPEE\nqX0XuPF2t1LvxVp49q+w2KeMT2IrK6g3sta+bIzpA1wAjALa4mrk5wHPW2uLgppLREQaZ/5M72Sv\nZRt3gI0kB79NsLu2wI5N2sQcL+Vl8Pbj3mPtOsNxZ4QbTyx07AE3TIH/3OZOH66pqgqe+iNkZbmn\nfRKeQDsKW2uLrLWPWmtvtdZ+MfJ1qhJ5EZH4sda/xOaY8a5mXpJD247Qrb/3mLraxM8nb7inX15O\nucKdsJoKuvSCG26H5q28x6sq4fG7YcW8cONKdzoeREQkxW1cCTs2eo+pi03y8TsN1q8dosRWyUH/\nnutd+sCIk8ONJ9a69YPrfw65PmVDlRXw6O9hzeJQw0prTVqPMcZcF/nlc9ba/dW+r5e19uGmzCki\nIk3j11u+a5+6D4aRxJQ32jt5XLcMSorcoT8Sng9egIP7vcdOvxoyMsONJww9B7qDpR76hXeHrIoy\neOQ3cN1t0HdI+PGlm6Y+XH0QsMBHwP5q39fFRO5RMi8iEpKKcljk0xR45CR3GJEkl16DXHeUgzXq\nlqsqYeVCGHZifOJKRwf2wKyXvcf6DPHf45AK+uTDNT+B//7S7RmoqazEjd0wxf2ZldhpajJ/Ey4x\n3xz5/sZgwhERkSAVzoHiA7WvZ2TAMSn2+D9dZGS6TcsL3q09tvxTJfNhmvGU98o0wOnXpv6H5f5D\n4eofuVV4r4PLSovh4Ttdr/ruPns9JHpNSuattQ/W+P6hQKIREZFA+W1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vIptTrcc9a4A+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\nWU39PxES/erT8nSrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a21d65080>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 377
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.plot(\n",
    "    Ms[:-1], (kernel_shap_std[:-1] / kernel_shap_m[:-1])*100,\n",
    "    label=\"Kernel SHAP\",\n",
    "    color=\"#7C52FF\", linewidth=3\n",
    ")\n",
    "pl.plot(\n",
    "    Ms[:-1], (ime_std[:-1] / ime_m[:-1])*100, \"--\",\n",
    "    label=\"IME\", color=\"#7C52FF\", linewidth=3\n",
    ")\n",
    "pl.ylabel(\"Std. deviation as % of magnitude\")\n",
    "pl.xlabel(\"# of features\")\n",
    "pl.legend(loc=\"upper left\")\n",
    "#pl.savefig(\"std_dev.pdf\")\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.017660769984570657"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(kernel_shap_std[:-1] / kernel_shap_m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.019387321525750678"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(kernel_shap_std[:-1] / kernel_shap_m[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.041465549361301236"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(ime_std[:-1] / ime_m[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a8c8s/hTg4F5Xa39wX/7H/OOFF/KWpsQHgi/zUTJfCtbaHcaY/wFjUTIvIhIT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\n4eofuVV4r4PLSovh4Ttdr/ruPns9JHpNSuattQ/W+P6hQKIREZFA+W1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vIptTrcc9a4A+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\nWU39PxES/erT8nSrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a21d65080>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 377
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.plot(\n",
    "    Ms[:-1], (ime_std[:-1] / ime_m[:-1])*100,\n",
    "    label=\"IME\", color=\"#7C52FF\", linewidth=3\n",
    ")\n",
    "pl.plot(\n",
    "    Ms[:-1], (kernel_shap_std[:-1] / kernel_shap_m[:-1])*100,\n",
    "    label=\"Kernel SHAP\",\n",
    "    color=\"#7C52FF\", linewidth=3\n",
    ")\n",
    "pl.plot(\n",
    "    Ms[:-1], (ime_std[:-1] / ime_m[:-1])*100, \"--\",\n",
    "    label=\"IME\", color=\"#7C52FF\", linewidth=3\n",
    ")\n",
    "pl.ylabel(\"Std. deviation as % of magnitude\")\n",
    "pl.xlabel(\"# of features\")\n",
    "pl.legend(loc=\"upper left\")\n",
    "#pl.savefig(\"std_dev.pdf\")\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VGtVVpFVmHXq0sNWSCktKmbs2CkvEljn1RqgXSH4OboYFL7qNJ5K2L4OJP/fH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\nSpoz9+UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a13d30b38>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 393
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.plot(Ms, kernel_shap_std / kernel_shap_m)\n",
    "pl.plot(Ms, ime_std / ime_m)\n",
    "pl.ylabel(\"minutes of runtime\")\n",
    "pl.xlabel(\"# of features\")\n",
    "pl.plot()\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 320.80395406,  552.44233763,  684.03119462,  841.14715592,\n",
       "       1016.57842226, 1226.48211761, 1450.68094181, 1690.75300588,\n",
       "       1728.4646152 ])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(sample_times) / np.array(tree_shap_times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oYkhJUumoXQLP3QZPXgeLZufq1S3hwJ/CAT+FFm2yyydJTciDwCRJleW94ckp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\nPAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a16822320>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 384
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.plot(Ms, np.array(tree_shap_times)*10000 / (60*60))\n",
    "pl.plot(Ms, np.array(sample_times)*10000 / (60*60))\n",
    "pl.ylabel(\"hours of runtime\")\n",
    "pl.xlabel(\"# of features\")\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8d5H5/emnHMM+3tqOPzVv+nVJ3rXYSrXWKxaa3t7pv/wY6gEAgDXT5X70h/9Q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\nm+EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a212c1d68>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 381
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.semilogy(Ms, tree_shap_times)\n",
    "pl.semilogy(Ms, sample_times)\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3761.9642857142862"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2.1067/0.00056"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0020101070404052734"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = time.time()\n",
    "model.predict(xgboost.DMatrix(X[:100,:]))\n",
    "time.time() - s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "M 20\n",
      "0.0007393969481046989 0.08655239076881058 0.008542767467621969\n",
      "KernelExplainer 1.5829526567459107\n",
      "0.0034187560235558454 0.08727476235122503 0.03917233265898269\n",
      "IMEExplainer 0.40989099979400634\n",
      "\n",
      "M 30\n",
      "0.0012877561556381184 0.10746270935466279 0.0119832839072398\n",
      "KernelExplainer 2.7619515800476075\n",
      "0.003955039524908129 0.10750344677696065 0.03678988575234914\n",
      "IMEExplainer 0.6094264173507691\n",
      "\n",
      "M 40\n"
     ]
    }
   ],
   "source": [
    "N = 1000\n",
    "X_full = np.random.randn(N, 100)\n",
    "y = np.random.randn(N)\n",
    "\n",
    "tree_shap_times = []\n",
    "kernel_shap_times = []\n",
    "ime_times = []\n",
    "tree_shap_std = []\n",
    "kernel_shap_std = []\n",
    "kernel_shap_m = []\n",
    "ime_std = []\n",
    "ime_m = []\n",
    "for M in [20,30,40,50,60,70,80,90,100]:#,30,40,50]:\n",
    "    print(\"\\nM\", M)\n",
    "    X = X_full[:,:M]\n",
    "    \n",
    "    model = xgboost.train({\"eta\": 1}, xgboost.DMatrix(X, y), 1000)\n",
    "\n",
    "    def f(x):\n",
    "        return model.predict(xgboost.DMatrix(x))\n",
    "    \n",
    "    e = shap.TreeExplainer(model)\n",
    "    start = time.time()\n",
    "    e.shap_values(X)\n",
    "    iter_time = (time.time() - start)/X.shape[0]\n",
    "    tree_shap_times.append(iter_time)\n",
    "    tree_shap_std.append(0)\n",
    "    \n",
    "    e = shap.KernelExplainer(f, X.mean(0).reshape(1,M))\n",
    "    nsamples = 1000 * M\n",
    "    start = time.time()\n",
    "    out = np.vstack([e.shap_values(X[:1,:], silent=True, nsamples=nsamples) for i in range(50)])\n",
    "    std_dev = out.std(0)[:-1].mean()\n",
    "    mval = np.abs(out.mean(0))[:-1].mean()\n",
    "    kernel_shap_m.append(mval)\n",
    "    iter_time = (time.time() - start)/50\n",
    "    kernel_shap_times.append(iter_time)\n",
    "    kernel_shap_std.append(std_dev)\n",
    "    print(std_dev, mval, std_dev / mval)\n",
    "    print(\"KernelExplainer\", iter_time)\n",
    "    \n",
    "    e = IMEExplainer(f, X.mean(0).reshape(1,M))\n",
    "    nsamples = 1000 * M\n",
    "    start = time.time()\n",
    "    out = np.vstack([e.shap_values(X[:1,:], silent=True, nsamples=nsamples) for i in range(50)])\n",
    "    std_dev = out.std(0)[:-1].mean()\n",
    "    mval = np.abs(out.mean(0))[:-1].mean()\n",
    "    ime_m.append(mval)\n",
    "    iter_time = (time.time() - start)/50\n",
    "    ime_times.append(iter_time)\n",
    "    ime_std.append(std_dev)\n",
    "    print(std_dev, mval, std_dev / mval)\n",
    "    print(\"IMEExplainer\", iter_time)\n",
    "\n",
    "ime_std = np.array(ime_std)\n",
    "ime_m = np.array(ime_m)\n",
    "kernel_shap_std = np.array(kernel_shap_std)\n",
    "kernel_shap_m = np.array(kernel_shap_m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00341876, 0.00395504, 0.00228658, 0.00303863, 0.00220854,\n",
       "       0.0014588 , 0.00276436, 0.00156815, 0.00030343])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ime_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.08727476, 0.10750345, 0.08316281, 0.07246471, 0.04027761,\n",
       "       0.04097318, 0.05724012, 0.03294186, 0.02315394])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ime_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.39396948e-04, 1.28775616e-03, 8.63459855e-04, 1.29795187e-03,\n",
       "       1.82790054e-03, 4.39236264e-04, 1.71100994e-03, 6.29363519e-04,\n",
       "       8.80791006e-05])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kernel_shap_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.08655239, 0.10746271, 0.08271485, 0.07177752, 0.04009051,\n",
       "       0.04008376, 0.05701191, 0.03229693, 0.02288745])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kernel_shap_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:59<00:00,  6.11s/it]\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TreeExplainer 0.5667633771896362\n",
      "KernelExplainer 1.6998610496520996\n",
      "IMEExplainer 36.06944036483765\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:43<00:00,  4.03s/it]\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TreeExplainer 0.5962720394134522\n",
      "KernelExplainer 2.410202980041504\n",
      "IMEExplainer 24.168269634246826\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [04:23<00:00, 26.14s/it]\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TreeExplainer 0.5785245656967163\n",
      "KernelExplainer 3.6281538009643555\n",
      "IMEExplainer 170.16926431655884\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [06:38<00:00, 41.04s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TreeExplainer 0.5870000839233398\n",
      "KernelExplainer 5.659902095794678\n",
      "IMEExplainer 254.79452514648438\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "tree_shap_times = []\n",
    "kernel_shap_times = []\n",
    "ime_times = []\n",
    "nreps = 10\n",
    "\n",
    "N = 1000\n",
    "X_full = np.random.randn(N, 20)\n",
    "y = np.random.randn(N)\n",
    "\n",
    "for M in range(4,8):\n",
    "    ts = []\n",
    "    tree_shap_time = 0\n",
    "    kernel_shap_time = 0\n",
    "    ime_time = 0\n",
    "    for k in tqdm(range(nreps)):\n",
    "#         print()\n",
    "         #+ ((X > 0).sum(1) % 2)\n",
    "        X = X_full[:,:M]\n",
    "\n",
    "        model = xgboost.train({\"eta\": 1}, xgboost.DMatrix(X, y), 1000)\n",
    "\n",
    "        def f(x):\n",
    "            return model.predict(xgboost.DMatrix(x))\n",
    "\n",
    "\n",
    "        start = time.time()\n",
    "        shap_values = shap.TreeExplainer(model).shap_values(X)\n",
    "        tree_shap_time += time.time() - start\n",
    "#         print(\"Tree SHAP:\", tree_shap_time, \"seconds\")\n",
    "\n",
    "        shap_stddev = shap_values.std(0)[:-1].mean()\n",
    "\n",
    "#         print(\"mean std dev of SHAP values over samples:\", shap_stddev)\n",
    "\n",
    "        e = shap.KernelExplainer(f, X.mean(0).reshape(1,M))\n",
    "        nsamples = 200\n",
    "#         print(shap_stddev/20)\n",
    "        for j in range(2000):\n",
    "            #print(nsamples)\n",
    "            start = time.time()\n",
    "            std_dev = np.vstack([e.shap_values(X[:1,:], silent=True, nsamples=nsamples) for i in range(50)]).std(0)[:-1].mean()\n",
    "            iter_time = (time.time() - start)/50\n",
    "            #print(std_dev)\n",
    "            if std_dev < shap_stddev/20:\n",
    "#                 print(\"KernelExplainer\", nsamples)\n",
    "#                 print(\"KernelExplainer\", std_dev)\n",
    "#                 print(\"KernelExplainer\", iter_time, \"seconds\")\n",
    "                kernel_shap_time += iter_time * 1000\n",
    "                break\n",
    "            nsamples += int(nsamples * 0.5)\n",
    "\n",
    "        e = IMEExplainer(f, X.mean(0).reshape(1,M))\n",
    "        nsamples = 200\n",
    "        for j in range(2000):\n",
    "        #     print()\n",
    "        #     print(nsamples)\n",
    "            start = time.time()\n",
    "            std_dev = np.vstack([e.shap_values(X[:1,:], silent=True, nsamples=nsamples) for i in range(50)]).std(0)[:-1].mean()\n",
    "        #     print(\"time\", (time.time() - start)/50)\n",
    "        #     print(std_dev)\n",
    "            iter_time = (time.time() - start)/50\n",
    "            if std_dev < shap_stddev/20:\n",
    "#                 print(\"IMEExplainer\", nsamples)\n",
    "#                 print(\"IMEExplainer\", std_dev)\n",
    "#                 print(\"IMEExplainer\", iter_time, \"seconds\")\n",
    "                ime_time += iter_time * 1000\n",
    "                break\n",
    "            nsamples += int(nsamples * 0.5)\n",
    "\n",
    "    tree_shap_times.append(tree_shap_time / nreps)\n",
    "    kernel_shap_times.append(kernel_shap_time / nreps)\n",
    "    ime_times.append(ime_time / nreps)\n",
    "    print(\"TreeExplainer\", tree_shap_times[-1])\n",
    "    print(\"KernelExplainer\", kernel_shap_times[-1])\n",
    "    print(\"IMEExplainer\", ime_times[-1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.035476692"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(xgboost.DMatrix(X)).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.35859036,  0.02820558, -0.0797407 , ...,  0.257011  ,\n",
       "         0.14601958, -0.03547668],\n",
       "       [-0.13089252,  0.05794828, -0.25855556, ...,  0.13599055,\n",
       "         0.05735664, -0.03547668],\n",
       "       [-0.06776153,  0.10805923, -0.15713027, ..., -0.03505304,\n",
       "        -0.126518  , -0.03547668],\n",
       "       ...,\n",
       "       [ 0.2136814 ,  0.26080823, -0.28865245, ...,  0.37536186,\n",
       "         0.0192902 , -0.03547668],\n",
       "       [-0.26988652, -0.4866938 ,  0.08402091, ..., -0.9819344 ,\n",
       "         0.10559861, -0.03547668],\n",
       "       [ 0.2289274 ,  0.34278524,  0.04976799, ...,  0.17089835,\n",
       "        -0.2589223 , -0.03547668]], dtype=float32)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap.TreeExplainer(model).shap_values(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03536954622922005"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e = shap.KernelExplainer(f, X.mean(0).reshape(1,M))\n",
    "np.vstack([e.shap_values(X[:1,:], silent=True, nsamples=100) for i in range(50)]).std(0)[:-1].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.01028014048933983\n",
      "200\n",
      "time 0.017876300811767578\n",
      "0.030893178278808088\n",
      "240\n",
      "time 0.01951019763946533\n",
      "0.031826491957925064\n",
      "288\n",
      "time 0.020209121704101562\n",
      "0.02404333546749491\n",
      "345\n",
      "time 0.026800222396850586\n",
      "0.02191601539734573\n",
      "414\n",
      "time 0.03098787784576416\n",
      "0.019460386139078127\n",
      "496\n",
      "time 0.035751018524169925\n",
      "0.016387066172673846\n",
      "595\n",
      "time 0.04151914119720459\n",
      "0.014285933327475487\n",
      "714\n",
      "time 0.04223478317260742\n",
      "0.007730075891320169\n",
      "714\n"
     ]
    }
   ],
   "source": [
    "e = shap.KernelExplainer(f, X.mean(0).reshape(1,M))\n",
    "nsamples = 200\n",
    "print(shap_stddev/20)\n",
    "for j in range(2000):\n",
    "    print(nsamples)\n",
    "    start = time.time()\n",
    "    std_dev = np.vstack([e.shap_values(X[:1,:], silent=True, nsamples=nsamples) for i in range(50)]).std(0)[:-1].mean()\n",
    "    iter_time = time.time() - start)/50\n",
    "    print(std_dev)\n",
    "    if std_dev < shap_stddev/20:\n",
    "        print(nsamples)\n",
    "        break\n",
    "    nsamples += int(nsamples * 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.01028014048933983\n",
      "200\n",
      "time 0.00873462200164795\n",
      "0.11283049739292224\n",
      "240\n",
      "time 0.008001022338867188\n",
      "0.11367197853719405\n",
      "288\n",
      "time 0.011501140594482422\n",
      "0.1015653587243526\n",
      "345\n",
      "time 0.009341177940368652\n",
      "0.09609185923926047\n",
      "414\n",
      "time 0.011126718521118163\n",
      "0.09085558541161791\n",
      "496\n",
      "time 0.013986682891845703\n",
      "0.07316596561338537\n",
      "595\n",
      "time 0.015461082458496095\n",
      "0.07250843647631118\n",
      "714\n",
      "time 0.01717233657836914\n",
      "0.06397322007857624\n",
      "856\n",
      "time 0.022870540618896484\n",
      "0.05976986733191383\n",
      "1027\n",
      "time 0.023901219367980956\n",
      "0.05245597953754562\n",
      "1232\n",
      "time 0.027827000617980956\n",
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      "0.044717441080350424\n",
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      "time 0.03991847991943359\n",
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      "2127\n",
      "time 0.049641480445861814\n",
      "0.03812949961499427\n",
      "2552\n",
      "time 0.055928120613098146\n",
      "0.035810585105934746\n",
      "3062\n",
      "time 0.06629895687103271\n",
      "0.03023017573129038\n",
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      "time 0.08427309989929199\n",
      "0.028151972927120038\n",
      "4408\n",
      "time 0.09465731620788574\n",
      "0.0269187187096272\n",
      "5289\n",
      "time 0.11998224258422852\n",
      "0.022239860334659148\n",
      "6346\n",
      "time 0.14299814224243165\n",
      "0.021938981389563468\n",
      "7615\n",
      "time 0.16221713542938232\n",
      "0.018939869158391444\n",
      "9138\n",
      "time 0.19562265872955323\n",
      "0.017731913242184698\n",
      "10965\n",
      "time 0.2298459815979004\n",
      "0.016393279654396718\n",
      "13158\n",
      "time 0.274345440864563\n",
      "0.014646114662805663\n",
      "15789\n",
      "time 0.3319106578826904\n",
      "0.013088638305933157\n",
      "18946\n",
      "time 0.39471452236175536\n",
      "0.012587389782775047\n",
      "22735\n",
      "time 0.47068305969238283\n",
      "0.010845919708344176\n",
      "27282\n",
      "time 0.5693935012817383\n",
      "0.009881009462255178\n",
      "27282\n"
     ]
    }
   ],
   "source": [
    "e = IMEExplainer(f, X.mean(0).reshape(1,M))\n",
    "nsamples = 200\n",
    "print(shap_stddev/20)\n",
    "for j in range(2000):\n",
    "    print()\n",
    "    print(nsamples)\n",
    "    start = time.time()\n",
    "    std_dev = np.vstack([e.shap_values(X[:1,:], silent=True, nsamples=nsamples) for i in range(50)]).std(0)[:-1].mean()\n",
    "    print(\"time\", (time.time() - start)/50)\n",
    "    print(std_dev)\n",
    "    if std_dev < shap_stddev/20:\n",
    "        print(nsamples)\n",
    "        break\n",
    "    nsamples += int(nsamples * 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "569.39"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.56939 * 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10860389655048514"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std([IMEExplainer(f, X.mean(0).reshape(1,M)).shap_values(X[:1,:], silent=True, nsamples=1000)[0,0] for i in range(10)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
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       " -0.02027189405855656,\n",
       " -0.021590607084915926,\n",
       " -0.018937654008019444,\n",
       " -0.01834817726407234,\n",
       " -0.01792343162344523,\n",
       " -0.02115901867004244,\n",
       " -0.019107028260820377,\n",
       " -0.02001855177068948,\n",
       " -0.019976099903129818,\n",
       " -0.013791710928271679,\n",
       " -0.021506799835474072,\n",
       " -0.01541739020484878]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[shap.KernelExplainer(f, X.mean(0).reshape(1,M)).shap_values(X[:1,:], silent=True, nsamples=1000)[0,0] for i in range(100)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  7%|▋         | 74/1000 [00:04<00:50, 18.27it/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-ad2b02590797>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mshap_values2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKernelExplainer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshap_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/slund1/projects/shap/shap/explainers/kernel.py\u001b[0m in \u001b[0;36mshap_values\u001b[0;34m(self, X, **kwargs)\u001b[0m\n\u001b[1;32m    124\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeep_index\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m                     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_to_instance_with_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumn_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_value\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m                 \u001b[0mexplanations\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexplain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m             \u001b[0;31m# vector-output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/slund1/projects/shap/shap/explainers/kernel.py\u001b[0m in \u001b[0;36mexplain\u001b[0;34m(self, incoming_instance, **kwargs)\u001b[0m\n\u001b[1;32m    258\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0msubset_size\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mnum_paired_subset_sizes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m                             \u001b[0mmask\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 260\u001b[0;31m                             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddsample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    261\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    262\u001b[0m                     \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/slund1/projects/shap/shap/explainers/kernel.py\u001b[0m in \u001b[0;36maddsample\u001b[0;34m(self, x, m, w)\u001b[0m\n\u001b[1;32m    346\u001b[0m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msynth_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0moffset\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 348\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaskMatrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnsamplesAdded\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    349\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernelWeights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnsamplesAdded\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    350\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnsamplesAdded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "def f(x):\n",
    "    return model.predict(xgboost.DMatrix(x))\n",
    "\n",
    "start = time.time()\n",
    "shap_values2 = shap.KernelExplainer(f, X.mean(0).reshape(1,M)).shap_values(X)\n",
    "print(time.time() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  4%|▍         | 41/1000 [00:13<05:22,  2.97it/s]"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-d6ca7fbbc83a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mIMEExplainer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshap_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/slund1/projects/shap/shap/explainers/kernel.py\u001b[0m in \u001b[0;36mshap_values\u001b[0;34m(self, X, **kwargs)\u001b[0m\n\u001b[1;32m    124\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeep_index\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m                     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_to_instance_with_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumn_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_value\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m                 \u001b[0mexplanations\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexplain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m             \u001b[0;31m# vector-output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-12-b3190dceb824>\u001b[0m in \u001b[0;36mexplain\u001b[0;34m(self, incoming_instance, **kwargs)\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX_masked\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnsamples_each\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mP\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m             \u001b[0mphi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minstance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnsamples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnsamples_each\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     48\u001b[0m         \u001b[0mphi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mphi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-12-b3190dceb824>\u001b[0m in \u001b[0;36mime\u001b[0;34m(self, j, f, x, X, nsamples)\u001b[0m\n\u001b[1;32m     62\u001b[0m             \u001b[0mpos\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minds\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m             \u001b[0mrind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m             \u001b[0mX_masked\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m             \u001b[0mX_masked\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpos\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrind\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpos\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m             \u001b[0mX_masked\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "IMEExplainer(f, X.mean(0).reshape(1,M)).shap_values(X)\n",
    "print(time.time() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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